The possibilities of applying logic to industrial processes to enhance their efficiency has long been recognized, even prior to the advent of electronic logic circuits and computers. One form of control that has advanced along with computer technology is pattern recognition. For example, U.S. Pat. No. 4,589,140--Bishop discloses a system that uses a video camera to compare magnified images of circuit boards. Similarly, a video surface inspection system disclosed in U.S. Pat. No. 4,675,730--Adomaitis et al. compares surface patterns to reference images and determines if an inspected surface falls within pre-determined minimum or maximum "grey level" values for the image.
Pattern recognition concepts are also useful when the "pattern" is not a video image of an object, but is instead a signal related to a characteristic. Thus, the sensed vibrational patterns of a machine tool can be analyzed using a pattern recognition system to determine tool breakage or other abnormal conditions as disclosed by U.S. Pat. No. 4,918,427--Thomas et al.; U.S. Pat. No. 4,853,860--Thomas; and U.S. Pat. No. 4,724,524--Thomas et al.
Recent progress in computing technology and the application of cognitive science to industrial processes has led to so-called expert systems that are capable of reasoning and decision making beyond simply identifying whether a particular pattern "matches" a stored pattern. For example, U.S. Pat. No. 4,916,625--Davidson et al. describes an expert system as a software program that contains information concerning a real world situation or environment that makes inferences about a given state or change. Thus, an expert system will generally contain a knowledge base and an inference mechanism to manipulate the data in the knowledge base and collected data. The system disclosed by Davidson et al. manages the task of optimizing a fiber spinning operation in real time by sensing the occurrence of events and applying a set of rules and inferences derived from the knowledge base to collected data. Expert systems are based on inductive reasoning or scientific knowledge and can therefore draw conclusions from data. As disclosed in U.S. Pat. No. 4,954,964--Singh, certain tasks normally undertaken exclusively by a human operator can be performed by a properly constructed expert system. The expert system disclosed by Singh accepts data concerning failed metal components and performs an analysis to determine the cause of the failure. By using a rule-based investigation, logically incorporated into a knowledge base, the cause of the failure can be systematically determined in much the same way a trained human failure expert makes similar determinations. Expert systems are also sometimes referred to as adaptive inference systems. For example, U.S. Pat. No. 5,000,714--Stark et al. discloses a system that locates faults in electrical or electronic assemblies.
The above-identified systems all require making sequences of logical decisions using large volumes of data. Since these systems are applied to actual processes, it is desirable that the logic be completed in as short a time as possible. Ideally, expert systems and the like should not delay the processes which they control. Those of ordinary skill will readily appreciate that in most practical instances, the computing power required to achieve this goal is immense and rarely cost-effective. Conversely, if a limited amount of computer assets are available, the system operates far too slowly to be practical. For this reason, expert systems are frequently created using advanced data processing and computer techniques that reduce either the computational power required or increase the processing speed, or both. However, these expert systems ultimately rely on algorithmic computing, i.e., they must be programmed with a specific set of "rules."
Another technique for implementing a complex computational task such as that performed by algorithmic expert system software is the use of a neural network. The term "neural network" is derived from the structural similarity of such systems to biological neural networks and their ability to exhibit self-learning. One advantage of neural network computers is their general applicability to many types of diagnosis and classification problems. Neural networks can process a wide variety and a large number of inputs which must be correlated to produce an output. For example, U.S. Pat. No. 4,965,725--Rutenberg discloses a neural network-based system for automatically classifying the images of cells generated by a microscope, a task previously performed by human operators. Neural networks differ from algorithmic computers and the expert systems discussed above in that the goal of neural learning is not the formulation of an algorithm or a set of explicit rules. During learning, a neural network self-organizes to establish a global set of weighted connections between parallel processors which will result in an output for a given input that most closely corresponds to what the network is told is the correct output for that input. It is this adaptive acquisition of connection strengths that allows a neural network computer to behave as if it knows the rules.
While a neural network computer is being trained, errors, which are defined as the difference between the appropriate output for an exemplary input and the actual output for that input, are propagated backwards from the output through the neural network to the input portion of the system. The errors are utilized at each layer of processing elements by a training algorithm to readjust the interconnection weight so that a future presentation of the exemplary pattern will result in the appropriate output. Unknown input patterns can also be classified by a neural network by placing them into the exemplary category which most closely resembles the input. Neural network learning is disclosed, for example, in U.S. Pat. No. 4,916,654--Wood.
Although welding might typically be classified as an industrial process, it has been recognized that welding requires highly skilled personnel who have acquired a skill set that permits them to vary welding parameters to achieve consistent, high quality welds. It would be desirable, however, to automate welding processes in a manner that would impart the skill of an experienced welder to the automated system. Toward this end, others have attempted to automate certain aspects of various types of welding systems. For example, U.S. Pat. No. 4,561,059--Davis discloses a microprocessor controlled arc welding power supply. Systems disclosed in U.S. Pat. Nos. 4,995,087 and 4,998,005, both to Rathi et al., automate welding to the extent that the process parameters for welding a work piece are pre-determined by digitizing a video image of the work piece. Others have attempted to adaptively control welding processes in real time by observing the weld "puddle" of molten metal formed during the process. For example, U.S. Pat. No. 4,739,404--Richardson discloses controlling a welding process by observing the oscillations in the size of the weld pool. Another pattern recognition system that uses video images of the weld puddle is disclosed in U.S. Pat. No. 4,877,940--Bangs et al. Others have also disclosed systems for permitting the visual monitoring of a weld site by filtering the arc light from the image. Examples of such systems are disclosed in U.S. Pat. No. 4,868,649--Gaudin and U.S. Pat. No. 4,918,517--Burgoon, the latter of which is assigned to the assignee of the present invention.
It is also known that the shape or position of the arc itself can provide information concerning the welding process for purposes of automation as disclosed in U.S. Pat. No. 4,951,218--Okumura et al.
Within the field of welding generally, it is well known that it is often possible to rebuild or restore worn or broken components by applying one or more weld beads and machining or finishing the repaired area. Such rebuilding may be undertaken, for example, on cylindrical objects such as portions of turbine rotors. Methods of repairing, rebuilding or modifying steam turbine rotors are disclosed in, for example, U.S. Pat. Nos. 4,633,554; 4,897,519; and 4,903,888, all issued to Clark et al., and U.S. Pat. No. 4,893,388 to Amos et al., all of which are assigned to the assignee of the present invention. Another example is the use of welding may be used to rebuild the turbine rotor steeples which provide blade attachments on a turbine rotor. Cracks in the steeples severely limit the life of the rotor; therefore, to prevent possible consequential damage from blade loss during turbine operation, rotor discs must be built up to a predetermined contour by welding and new steeples machined into the rotor discs.
In rotor welding processes, a new disc is built up by continuously winding a relatively fine filler wire or weld wire, typically about 0.0625 inches (0.159 cm) in diameter, on to the surface of a rotor while it is rotating at about 30 min./revolution (0.033 revolutions/min.). Simultaneously, this wire strand is automatically welded side-by-side to previously welded strands, and a weld deposit is typically built up to a width of about 4-5 inches (10-13 cm) and a depth of about 3 inches (7.6 cm) or more. The weld build-up is preferably formed of fine filler wire to avoid overheating the disc material during welding. Overheating would create damage in heat-affected zones, resulting in, for example, poor corrosion properties and reduced durability. However, it will be readily appreciated that building up a surface with fine filler wire is time-consuming.
A partial diagrammatic representation of the above-described process is illustrated in FIG. 1. A torch 12, a power supply and source of shielding gas 16 generate a welding arc 10. During the lengthy welding process, an operator 200 must visually monitor the welding arc 10 by viewing it through a filter 20. Additionally, at the end of each winding and welding revolution, the operator 200 must reposition the torch 12 to begin welding the next row of wound-on filler wire 14. This repositioning step, called indexing the arc, takes just a few seconds. However, at present a dedicated operator 200 is required for monitoring and indexing each welding machine. Thus, most of the operator's time is spent watching the arc 10 for anomalies. However, such anomalies occur relatively infrequently because of the use of automated welding equipment. Consequently, operator boredom can lead to associated problems such as lapses in worker concentration, diminished weld quality, and low worker morale.
It is therefore an object of the present invention to provide a system for monitoring arc welding that can monitor an arc and detect anomalies during the welding process. A further object of the present invention is to provide such a welding system that has been taught to distinguish normal arc from abnormal arc using both audio and visual data.